Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.6
1.7
Journals
Social Sciences
Special Issues
Technology, Digital Transformation and Society
share announcement

Technology, Digital Transformation and Society

A special issue of Social Sciences (ISSN 2076-0760).

Deadline for manuscript submissions: 30 November 2025 | Viewed by 10570

Special Issue Editor

Prof. Dr. Ina Kayser

E-Mail Website
Guest Editor
Department of Business and Communication, IST University of Applied Sciences, 40233 Düsseldorf, Germany
Interests: technology acceptance; human-computer interaction; digital transformation

Special Issue Information

Dear Colleagues,

In today’s world, where digital technologies are integral to every aspect of our existence, the interplay of technology, digital transformation, and societal dynamics is critically important. This Special Issue titled “Technology, Digital Transformation and Society” aims to scrutinize the complex effects of digital evolution on societal frameworks, individual actions, and the worldwide community. Notably, the integration of artificial intelligence (AI) into the societal matrix offers both groundbreaking prospects and novel challenges. This Special Issue is dedicated to examining the significant influence of digital technologies, including AI, on societal organization, ethical frameworks, individual conduct, and the collective human experience. As AI progresses, it is reshaping what we consider possible, enhancing decision-making, transforming industries, and even shifting the ways we interact and perceive cultural norms.

This Special Issue serves as a forum for multidisciplinary discussions on both the hurdles and prospects that digital transformation, particularly the surge in AI technologies, presents. We seek scholarly contributions that investigate how technological innovations like AI impact social fairness, privacy, ethics, and cultural dynamics. Furthermore, this Special Issue will explore how digital technologies can promote inclusion, sustainability, and adaptability in societies undergoing swift transformations and facing new uncertainties.

Through a mix of empirical studies, theoretical exploration, and case studies, this Special Issue will spotlight groundbreaking practices, implications for policy, and future trends at the intersection of technology, digital change, and societal dynamics. Submissions are welcomed from a diverse range of fields, such as sociology, information technology, political science, education, and cultural studies. Our goal is to foster a holistic understanding of this intricate and evolving domain, encouraging contributions that dissect and envision the role of technology in shaping society.

Prof. Dr. Ina Kayser
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Social Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital transformation
  • societal impact
  • artificial intelligence
  • social equity
  • privacy and ethics
  • cultural dynamics
  • inclusivity in digital age
  • sustainability and technology
  • policy implications
  • future directions

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

12 pages, 222 KiB  
Article
Game Changer: Harnessing Artificial Intelligence in Sport for Development
by Louis Moustakas
Soc. Sci. 2025, 14(3), 174; https://doi.org/10.3390/socsci14030174 - 13 Mar 2025
Viewed by 564
Abstract
Sport for Development (SFD) leverages sports as a tool to support broader sustainable development goals, particularly in underserved communities worldwide. As Artificial Intelligence (AI) technology advances, its application in SFD offers both promising opportunities and significant challenges in areas such as curriculum design, [...] Read more.
Sport for Development (SFD) leverages sports as a tool to support broader sustainable development goals, particularly in underserved communities worldwide. As Artificial Intelligence (AI) technology advances, its application in SFD offers both promising opportunities and significant challenges in areas such as curriculum design, evaluation, and participant engagement. Through a qualitative survey of experts and practitioners analysed through Thematic Analysis (TA), this paper explores perspectives on the potential of AI to enhance the delivery and management of SFD initiatives, as well as potential risks and needs in the field. Key perceived benefits include compensating for deficient organisational capacities and supporting the performance of both administrative and conceptual tasks. Potential risks include the propagation of increasingly generic approaches to SFD programming, loss of critical thinking skills, and concerns around participant safeguarding. To mediate this, exchange, education, and SFD-specific policies are seen as crucial. Full article
(This article belongs to the Special Issue Technology, Digital Transformation and Society)
19 pages, 1330 KiB  
Article
From Traditional to Digital: Transforming Local Administrative Organization Workflows in Thailand Through Social Listening Tools
by Krisada Prachumrasee, Panpun Ronghanam, Kasipat Thonmanee, Pakpoom Phonsungnoen, Pathompohn Mangma, Prasongchai Setthasuravich and Grichawat Lowatcharin
Soc. Sci. 2024, 13(12), 666; https://doi.org/10.3390/socsci13120666 - 11 Dec 2024
Viewed by 1639
Abstract
Digital transformation offers transformative potential for public service delivery, yet many local administrative organizations (LAOs) in Thailand struggle with integrating digital tools effectively into their workflows. This study investigates the integration of social listening tools (SLTs) to enhance the efficiency and responsiveness of [...] Read more.
Digital transformation offers transformative potential for public service delivery, yet many local administrative organizations (LAOs) in Thailand struggle with integrating digital tools effectively into their workflows. This study investigates the integration of social listening tools (SLTs) to enhance the efficiency and responsiveness of public service delivery in Thailand’s LAOs. The primary goal is to redesign traditional, manual workflows through the development of a digital-by-design framework, addressing inefficiencies in public engagement and service provision. Employing a mixed-method approach, this research combines interviews and focus groups with municipal staff from four municipalities in Northeast Thailand to identify challenges and co-create solutions. The redesigned workflow integrates digital practices into existing organizational structures and achieves a significant 282% improvement in efficiency, measured in transactions per manpower-hour. Additionally, the new process enhances operational speed, responsiveness, and public engagement. To ensure sustainability, this study recommends a phased implementation strategy and consistent staff training. This research contributes to the public administration literature by providing a practical, scalable framework for digital transformation in local governance. It underscores the potential of SLTs to modernize public sector workflows, enabling more dynamic, responsive, and citizen-centric interactions between LAOs and the communities they serve. Full article
(This article belongs to the Special Issue Technology, Digital Transformation and Society)
►▼ Show Figures

Figure 1

18 pages, 494 KiB  
Article
Impact of Motivation Factors for Using Generative AI Services on Continuous Use Intention: Mediating Trust and Acceptance Attitude
by Sangbum Kang, Yongjoo Choi and Boyoung Kim
Soc. Sci. 2024, 13(9), 475; https://doi.org/10.3390/socsci13090475 - 9 Sep 2024
Cited by 4 | Viewed by 6616
Abstract
This study aims to empirically analyze the relationship between the motivational factors of generative AI users and the intention to continue using the service. Accordingly, the motives of users who use generative AI services are defined as individual, social, and technical motivation factors. [...] Read more.
This study aims to empirically analyze the relationship between the motivational factors of generative AI users and the intention to continue using the service. Accordingly, the motives of users who use generative AI services are defined as individual, social, and technical motivation factors. This research verified the effect of these factors on intention to continue using the services and tested the meditating effect of trust and acceptance attitude. We tested this through verifying trust and acceptance attitudes. An online survey was conducted on language-based generative AI service users such as OpenAI’s ChatGPT, Google Bard, Microsoft Bing, and Meta-Lama, and a structural equation analysis was conducted through a total of 356 surveys. As a result of the analysis, individual, social, and technical motivational factors all had a positive (+) effect on trust and acceptance attitude on the attitude toward accepting generative AI services. Among them, individual motivation such as self-efficacy, innovation orientation, and playful desire were found to have the greatest influence on the formation of the acceptance attitude. In addition, social factors were identified as the factors that have the greatest influence on trust in the use of generative AI services. When it comes to using generative AI, it was confirmed that social reputation or awareness directly affects the trust in usability. Full article
(This article belongs to the Special Issue Technology, Digital Transformation and Society)
►▼ Show Figures

Figure 1

Review

Jump to: Research

20 pages, 793 KiB  
Review
Building and Eroding the Citizen–State Relationship in the Era of Algorithmic Decision-Making: Towards a New Conceptual Model of Institutional Trust
by Jaana Parviainen, Anne Koski, Laura Eilola, Hannele Palukka, Paula Alanen and Camilla Lindholm
Soc. Sci. 2025, 14(3), 178; https://doi.org/10.3390/socsci14030178 - 17 Mar 2025
Viewed by 508
Abstract
In liberal welfare states, algorithmic decision-making systems are being increasingly deployed, impacting the citizen–state relationship in a multitude of positive and negative ways. This theoretical paper aims to develop a novel conceptual model—the institutional trust model—to analyse how the implementation of automated systems [...] Read more.
In liberal welfare states, algorithmic decision-making systems are being increasingly deployed, impacting the citizen–state relationship in a multitude of positive and negative ways. This theoretical paper aims to develop a novel conceptual model—the institutional trust model—to analyse how the implementation of automated systems erodes or strengthens institutional trust between policymakers and citizens. In this approach, institutional trust does not simply mean public trust in institutions (though it is an important component of democratic societies); instead, it refers to the responsive interactions between governmental institutions and citizens. Currently, very little is known about policymakers’ trust or distrust in automated systems and how their trust or distrust in citizens is reflected in their interest in implementing these systems in public administration. By analysing a sample of recent studies on automated decision-making, we explored the potential of the institutional trust model to identify how the four dimensions of trust can be used to explore the responsive relationship between citizens and the state. This article contributes to the formulation of research questions on automated decision-making in the future, underlining that the impact of automated systems on the socio-economic rights of marginalised citizens in public services and the policymakers’ motivations to deploy automated systems have been overlooked. Full article
(This article belongs to the Special Issue Technology, Digital Transformation and Society)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop